dinvgamma: Inverse gamma distribution
In jarad/conjugateBayes: Performs conjugate Bayesian analyses.

Description Usage Arguments Details Value See Also Examples

Probability density function, cumulative distribution function, quantile function, and random variable generator for the inverse gamma distribution with parameters shape and scale.

dinvgamma(x, shape, scale = 1, log)

qinvgamma(p, shape, scale = 1, lower.tail = TRUE, log.p = FALSE)

pinvgamma(q, shape, scale = 1, lower.tail = TRUE, log.p = FALSE)

rinvgamma(n, shape, scale = 1)

`x, q`	vector of quantiles
`shape, scale`	shape and scale parameters. Must be positive.
`log, logp`	logical; if TRUE, probabilities/densities p are returned as log(p)
`p`	vector of probabilities
`lower.tail`	logical; if TRUE (default), probabilities are p[X\le X], otherwise, P[X>x]
`n`	number of observations. If length(n) > 1, the length is taken to be the number required.

The inverse gamma distribution with parameters shape = a and scale = b has density

f(x) = b^a / Gamma(a) x^-a-1 exp(-b/x)

for x\ge 0, a>0, b>0. (Gamma(a) is the function implemented by R's gamma() and defined in its help.)

The mean is \fracba-1 (if a>1) and the variance is \fracb^2(a-1)^2(a-2) (if a>2).

These functions are all based on the gamma distribution. Please see the documentation for issues using the gamma distribution, e.g. issues when the shape parameter is small.

dgamma gives the density, pgamma gives the distribution function, qgamma gives the quantile function, and rgamma generates random deviates.

dgamma